A deep learning model for predicting buy and sell recommendations in stock exchange of Thailand using long short-term memory

Nowadays, the stock price prediction has been one of the most challenging problem to the AI research community. Most prediction techniques concentrate on forecasting the future prices of stocks based on conventional Machine learning techniques. However, these techniques cannot capture long term dependencies in stock price data. Therefore, they cannot consider the relation between the current predicted data and the previous data in stock data. This research adopts deep learning techniques for predicting buy and sell recommendations in Stock Exchange of Thailand using Long Short-Term Memory. The proposed model can capture long term dependencies in stock price data in order to enhance the prediction accuracy. The accuracy of the proposed model is evaluated on five Stock Exchange of Thailand (SET) stocks, between 5 January 2015 and 29 December 2017, and compared the results with support vector Machine, multilayer perceptron, decision tree, random forest, logistic regression and k-nearest neighbors. The experimental results signify that the proposed model can outperform all comparative models.